Self-supervised learning for medical image analysis using image context restoration-Reference-Cited by-同舟云学术

Self-supervised learning for medical image analysis using image context restoration

Published:2019-12 Issue: Volume:58 Page:101539
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Chen Liang^ORCID,Bentley Paul,Mori Kensaku^ORCID,Misawa Kazunari,Fujiwara Michitaka,Rueckert Daniel^ORCID

Funder

Wellcome Trust

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference54 articles.

1. Learning to see by moving;Agrawal,2015

2. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection;Bakas;Cancer Imag. Arch,2017

3. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features;Bakas;Sci. Data,2017

4. Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound;Baumgartner;IEEE Trans. Med. Imag.,2017

5. Greedy layer-wise training of deep networks;Bengio,2007

Cited by 358 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision;Medical Image Analysis;2025-01

2. Rethinking masked image modelling for medical image representation;Medical Image Analysis;2024-12

3. Self-supervised machine learning approach for autism detection in young children using MEG signals;Biomedical Signal Processing and Control;2024-12

4. Dual-domain MIM based contrastive learning for CAD of developmental dysplasia of the hip with ultrasound images;Biomedical Signal Processing and Control;2024-11

5. Enhancing the vision–language foundation model with key semantic knowledge-emphasized report refinement;Medical Image Analysis;2024-10